There are some conventional approaches for detecting objects based on input images. According to one approach, optical flow is first calculated from captured images and a part corresponding to an object is then extracted from the area having the same motion component. Since moving objects in the image may be easily detected by this approach, some object detection apparatuses based on this approach are known in the art (for example, Japanese Patent Application Unexamined Publication (Kokai) No. 7-249127).
However, when an imaging device itself for capturing images moves (for example, an imaging device is mounted on an automobile), it is difficult to detect moving objects by above-described approach because optical flow is also generated by the motion of the imaging device. In this case, removing motion component generated by the motion of the imaging device from the calculated optical flow enables the object detection apparatus to detect moving objects in the captured image more accurately. For example, a method for detecting the movement is disclosed in Japanese Patent Application Unexamined Publication (Kokai) No. 2000-242797 wherein diffusion coefficients used in detecting optical flow in the image with gradient method is variable. According to the method, the diffusion coefficients may be varied with the addition of some conditions instead of the diffusion coefficients being constant like conventional approaches. By this method, noise tolerance may be improved and the differential of optical flow at the boundary of objects may be emphasized.
This method enables the object detection apparatus to calculate optical flow of the moving object more accurately. However, optical flow of stationary objects in background of the input images would not be compensated because they would be considered as a background by this method. Therefore, it is impossible by this method to detect stationary objects accurately.
It is actually possible to calculate the optical flow from each object even though the stationary objects in the stationary background are observed from a mobile unit. However, it is difficult to segment the optical flow of the objects from that of the background and such accurate segregating technique has not been realized.
Therefore, there exists a need for an object detection approach for accurately detecting both stationary objects and moving objects included in an image captured from a moving imaging device using optical flow.